Early dynamics of chronic myeloid leukemia on nilotinib predicts deep molecular response

Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. Although ABL1-specific tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML, the TKI efficacy depends on the individual patient. In this work, we found that the patients with different nilotinib responses can be classified by using the estimated parameters of our simple dynamical model with two common laboratory findings. Furthermore, our proposed method identified patients who failed to achieve a treatment goal with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs.


Supplementary note 1. EUTOS score-based and ELTS score-based DMR prediction
To understand the characteristics of the patients whose deep molecular response (DMR) predictions based on the EUTOS score or the EUTOS long-term survival (ELTS) score failed, we investigated the patient characteristics of the variables to calculate the scores (Supplementary figures 6-8). For readability, here we repeat the definitions of the EUTOS score and the ELTS score, and the variables used in the scores. The two scores are defined as follows: EUTOS score ∶= 4 × spleen size below costal margin (cm) + 7 × basophil ratio (%), ELTS score ∶= 0.0025 × G age in completed years 10 K ! + 0.0615 × spleen size below costal margin (cm) + 0.1052 × blasts in peripheral blood (%) If a patient has a high score, the patient is considered as "high risk" or "intermediate risk", which corresponds to "prediction as non-DMR" in this study. If a patient has a low score, the patient is considered as "low risk", which corresponds to "prediction as DMR". According to the score definition, the scores are monotonically increasing functions of the variables. It means that these scores are designed so that patients with large variable values tend to be predicted as non-DMR.
To investigate the characteristics of the patients whose prediction based on the EUTOS score or ELTS score failed, we classified the N-road study patients into four subsets based on the prediction results. The four subsets are as follows: predicted as true positive (TP), false positive (FP), false negative (FN), and true negative (TN) cases. The numbers of patients in the cases are indicated in the confusion matrices shown in Supplementary figure 6. In the N-road dataset, compared with our method, the EUTOS score-based prediction tends to classify patients into FP cases, while the ELTS score-based prediction tends to classify patients into FN cases (Supplementary figure 6).
Please note that, in the analyzed dataset, no patients were classified as FP cases by the ELTS score- , respectively. As we explained above, the EUTOS score is calculated by two variables, spleen size and basophil ratio. The score is expressed by a sum of each variable's monotonically increasing linear functions. It means that the EUTOS score is designed so that patients with smaller variable values tend to be classified as "low risk" corresponding to "prediction as DMR". Patients with larger variable values tend to be classified as "high risk" corresponding to "prediction as non-DMR". However, the average spleen size of FN patient(s) (patients who achieved DMR but were predicted as non-DMR) is larger than that of TN patients (patients who did not achieve DMR and were correctly predicted as non-DMR) as shown in Supplementary  This tendency was also confirmed in the case of basophil ratio. As above, actual observations of DMR and the design of EUTOS scores showed some discordance. Therefore, the EUTOS score-based prediction did not work well. However, for 21 patients, the WBC count and/or IS data were incomplete at a certain time point.
Thus, in this study, we used only the remaining 32 patient data. represents the estimated dynamics based on our proposed model. Our model approximated all measurement data sufficiently.

Supplementary figure 3. Effectiveness of the EUTOS score, ELTS score, and European
LeukemiaNet guideline criteria for MR4.0, MR4.5, and CMR. (a-c) Based on the approach described in Fig.1  system/guideline. A number indicated in a cell of the matrices is the number of patients corresponding to the predicted and actual conditions. We note that the classification performance scores (accuracy, sensitivity, specificity, and F1 score) shown in Fig.4